{smcl}
{com}{sf}{ul off}{txt}{.-}
      name:  {res}<unnamed>
       {txt}log:  {res}C:\Users\swhitt\Desktop\TPV Children within Insurgency Replication Files\TPV Children within insurgency replication data long format log file.smcl
  {txt}log type:  {res}smcl
 {txt}opened on:  {res}11 Feb 2024, 17:33:35

{com}. do "C:\Users\swhitt\Desktop\TPV Children within Insurgency Replication Files\TPV Children within insurgency replication data long format do file.do"
{txt}
{com}. *Retribution versus Rehabilitation for Children within Insurgency: Public Attitudes toward ISIS-affiliated Youth in Mosul, Iraq
. 
. *Replication Instructions
. 
. *Vera Mironova and Sam Whitt
. 
. *Below are instructions for replicating all manuscript and online appendix tables and figures in STATA using the LONG format of the dataset "TPV Children within insurgency replication data wide format.dta". Please contact Sam Whitt (swhitt@highpoint.edu) for questions regarding data replication. Use the appropriate "long format" do file to replicated different components of the manuscript.
. 
. *Note: You may need to install STATA packages for cibar, catcibar  and iebaltab commands. Use findit with command name to identify and download the appropriate packets to install. To install the catcibar command enter the following:
. *ssc install cibar
. *net install catcibar, from("https://aarondwolf.github.io/catcibar")
. 
. *Note: In addition, some graphs require additional formatting using filename.grec files with the graph play command. To format a graph, simply run the command to generate the graph in the do file in STATA, then open the "Graph Editor" in STATA and click on the GREEN "Play Recording" button, then select "Browse" to select the grec file from among Replication files. The name of the grec file is indicated in the note below the graph command in the do file for the specific graph you wish to format. This should automatically format the graph, which you may then save to a location of your choosing.
. 
. 
. *Manuscript Text
. 
. *Use wide format replication data for all text replication
. 
. *Manuscript Tables and Figures
. 
. *Figure 1. Determinants of adulthood (see wide format)
. 
. *Figure 1a. Age at which children should be punished as adults. (see wide format)
. 
. *Figure 1b. Determinants of adulthood (see wide format)
. 
. *Figure 2. Mean punishment preferences for adults and children
. cibar pun if pungroup<5, over1(pungroup)
{res}{txt}
{com}. 
. *Note additional formatting requires the "Figure 2 long formatting.grec" file with the command graph play "Figure 2 long formatting.grec"
. 
. *Figure 3. Punishment preferences with parental coercion treatments.
. 
. *Figure 3a. (see wide format)
. 
. *Figure 3b. (see wide format)
. 
. *Table 1. Punishment preferences for adults and children (OLS regression).
. 
. *Model 1 (Long format)
. reg pun fightertreatment##childtreatment  if pungroup<5, cluster(id)

{txt}Linear regression                               Number of obs     = {res}     1,427
                                                {txt}F(3, 356)         =  {res}  1919.67
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.7331
                                                {txt}Root MSE          =    {res} .63362

{txt}{ralign 97:(Std. err. adjusted for {res:357} clusters in {res:id})}
{hline 32}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 33}{c |}{col 45}    Robust
{col 1}                            pun{col 33}{c |} Coefficient{col 45}  std. err.{col 57}      t{col 65}   P>|t|{col 73}     [95% con{col 86}f. interval]
{hline 32}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 13}1.fightertreatment {c |}{col 33}{res}{space 2}  1.03994{col 45}{space 2} .0383083{col 56}{space 1}   27.15{col 65}{space 3}0.000{col 73}{space 4} .9646005{col 86}{space 3} 1.115279
{txt}{space 15}1.childtreatment {c |}{col 33}{res}{space 2} -1.88443{col 45}{space 2}  .037816{col 56}{space 1}  -49.83{col 65}{space 3}0.000{col 73}{space 4}-1.958801{col 86}{space 3}-1.810059
{txt}{space 31} {c |}
fightertreatment#childtreatment {c |}
{space 27}1 1  {c |}{col 33}{res}{space 2}  .553898{col 45}{space 2} .0463697{col 56}{space 1}   11.95{col 65}{space 3}0.000{col 73}{space 4}  .462705{col 86}{space 3} .6450909
{txt}{space 31} {c |}
{space 26}_cons {c |}{col 33}{res}{space 2} 3.741573{col 45}{space 2} .0321339{col 56}{space 1}  116.44{col 65}{space 3}0.000{col 73}{space 4} 3.678377{col 86}{space 3} 3.804769
{txt}{hline 32}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. 
. *Model 2 (long format)
. reg pun fightertreatment##childtreatment ageofpunishment  female age education professional laborer unemployed income i.religion revimpreligion i.ethnicity revimpethnicity if pungroup<5, cluster(id)

{txt}Linear regression                               Number of obs     = {res}     1,427
                                                {txt}F(19, 356)        =  {res}   352.12
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.7425
                                                {txt}Root MSE          =    {res} .62581

{txt}{ralign 97:(Std. err. adjusted for {res:357} clusters in {res:id})}
{hline 32}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 33}{c |}{col 45}    Robust
{col 1}                            pun{col 33}{c |} Coefficient{col 45}  std. err.{col 57}      t{col 65}   P>|t|{col 73}     [95% con{col 86}f. interval]
{hline 32}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 13}1.fightertreatment {c |}{col 33}{res}{space 2} 1.040094{col 45}{space 2} .0385486{col 56}{space 1}   26.98{col 65}{space 3}0.000{col 73}{space 4} .9642825{col 86}{space 3} 1.115906
{txt}{space 15}1.childtreatment {c |}{col 33}{res}{space 2}-1.884276{col 45}{space 2}  .038048{col 56}{space 1}  -49.52{col 65}{space 3}0.000{col 73}{space 4}-1.959103{col 86}{space 3}-1.809449
{txt}{space 31} {c |}
fightertreatment#childtreatment {c |}
{space 27}1 1  {c |}{col 33}{res}{space 2} .5537435{col 45}{space 2} .0466596{col 56}{space 1}   11.87{col 65}{space 3}0.000{col 73}{space 4} .4619803{col 86}{space 3} .6455067
{txt}{space 31} {c |}
{space 16}ageofpunishment {c |}{col 33}{res}{space 2} .0557429{col 45}{space 2} .0313225{col 56}{space 1}    1.78{col 65}{space 3}0.076{col 73}{space 4}-.0058575{col 86}{space 3} .1173433
{txt}{space 25}female {c |}{col 33}{res}{space 2}-.2161338{col 45}{space 2} .0891816{col 56}{space 1}   -2.42{col 65}{space 3}0.016{col 73}{space 4}-.3915229{col 86}{space 3}-.0407447
{txt}{space 28}age {c |}{col 33}{res}{space 2}-.0019126{col 45}{space 2} .0021183{col 56}{space 1}   -0.90{col 65}{space 3}0.367{col 73}{space 4}-.0060786{col 86}{space 3} .0022534
{txt}{space 22}education {c |}{col 33}{res}{space 2} .0195704{col 45}{space 2} .0189451{col 56}{space 1}    1.03{col 65}{space 3}0.302{col 73}{space 4} -.017688{col 86}{space 3} .0568288
{txt}{space 19}professional {c |}{col 33}{res}{space 2}-.0762401{col 45}{space 2} .0537137{col 56}{space 1}   -1.42{col 65}{space 3}0.157{col 73}{space 4}-.1818762{col 86}{space 3}  .029396
{txt}{space 24}laborer {c |}{col 33}{res}{space 2}  .017202{col 45}{space 2} .0500963{col 56}{space 1}    0.34{col 65}{space 3}0.732{col 73}{space 4}-.0813198{col 86}{space 3} .1157238
{txt}{space 21}unemployed {c |}{col 33}{res}{space 2} .0775954{col 45}{space 2} .0556248{col 56}{space 1}    1.39{col 65}{space 3}0.164{col 73}{space 4}-.0317991{col 86}{space 3} .1869899
{txt}{space 25}income {c |}{col 33}{res}{space 2} .0030104{col 45}{space 2}  .016308{col 56}{space 1}    0.18{col 65}{space 3}0.854{col 73}{space 4}-.0290617{col 86}{space 3} .0350826
{txt}{space 31} {c |}
{space 23}religion {c |}
{space 26}Shia  {c |}{col 33}{res}{space 2} -.049046{col 45}{space 2} .0759494{col 56}{space 1}   -0.65{col 65}{space 3}0.519{col 73}{space 4}-.1984119{col 86}{space 3} .1003199
{txt}{space 21}Christian  {c |}{col 33}{res}{space 2} .0219604{col 45}{space 2} .1028453{col 56}{space 1}    0.21{col 65}{space 3}0.831{col 73}{space 4}-.1803003{col 86}{space 3} .2242212
{txt}{space 25}Other  {c |}{col 33}{res}{space 2}  .018213{col 45}{space 2} .0769134{col 56}{space 1}    0.24{col 65}{space 3}0.813{col 73}{space 4}-.1330488{col 86}{space 3} .1694747
{txt}{space 31} {c |}
{space 17}revimpreligion {c |}{col 33}{res}{space 2} .0783873{col 45}{space 2} .0204134{col 56}{space 1}    3.84{col 65}{space 3}0.000{col 73}{space 4} .0382412{col 86}{space 3} .1185333
{txt}{space 31} {c |}
{space 22}ethnicity {c |}
{space 26}Kurd  {c |}{col 33}{res}{space 2}   .10167{col 45}{space 2} .0636666{col 56}{space 1}    1.60{col 65}{space 3}0.111{col 73}{space 4}-.0235399{col 86}{space 3} .2268798
{txt}{space 23}Turkmen  {c |}{col 33}{res}{space 2} .0780917{col 45}{space 2} .0562346{col 56}{space 1}    1.39{col 65}{space 3}0.166{col 73}{space 4}-.0325021{col 86}{space 3} .1886855
{txt}{space 25}Other  {c |}{col 33}{res}{space 2}-.0025185{col 45}{space 2} .0994095{col 56}{space 1}   -0.03{col 65}{space 3}0.980{col 73}{space 4}-.1980222{col 86}{space 3} .1929852
{txt}{space 31} {c |}
{space 16}revimpethnicity {c |}{col 33}{res}{space 2}-.0199693{col 45}{space 2}  .021367{col 56}{space 1}   -0.93{col 65}{space 3}0.351{col 73}{space 4}-.0619907{col 86}{space 3} .0220521
{txt}{space 26}_cons {c |}{col 33}{res}{space 2} 2.608114{col 45}{space 2} .5418355{col 56}{space 1}    4.81{col 65}{space 3}0.000{col 73}{space 4} 1.542513{col 86}{space 3} 3.673715
{txt}{hline 32}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. 
. *Model 3 (see wide format)
. 
. *Figure 4. Threat perception and rehabilitation prospects of a young child (see wide format)
. 
. *Table 2. Opposition to reintegration into society (OLS regression). (see wide format)
. 
. *Appendix Replication
. 
. *Mosul Sampling Locations (see wide format)
. 
. *Summary of Variables in Manuscript Analysis (see wide format)
. 
. *Figure 1 General Punishment Preferences for ISIS Fighters (Child/Adult)
. 
. twoway  (histogram pun if pungroup==1, discrete percent) (histogram pun if pungroup==3,discrete percent fcolor(none) lcolor(black))
{res}{txt}
{com}. 
. *Note additional formatting required
. 
. 
. *Figure 2 General Punishment Preferences for ISIS Workers (Child/Adult)
. 
. twoway  (histogram pun if pungroup==2, discrete percent) (histogram pun if pungroup==4,discrete percent fcolor(none) lcolor(black))
{res}{txt}
{com}. 
. *Note additional formatting required
. 
. 
. *Figure 3. Ahmad 10-year old fighter vignette (see wide format)
. 
. *Experimental Balance Tests across Treatment Groups (see wide format)
. 
. *Treatment Effect Estimation for Child Fighter with/wo Father Survey Vignette (Ahmad) (see wide format)
. 
. *Treatment Effect Estimation for Child of Local/Foreign Fighter Survey Vignette (Sami, Threat Perception Index) (see wide format)
. 
. *Manuscript Table 1. Robustness Check (Ordered Probit Regression)
. 
. *Model 1 (Long format)
. oprobit pun fightertreatment##childtreatment  if pungroup<5, cluster(id)

{res}{txt}Iteration 0:{space 2}Log pseudolikelihood = {res:-2175.0596}  
Iteration 1:{space 2}Log pseudolikelihood = {res:-1343.4673}  
Iteration 2:{space 2}Log pseudolikelihood = {res:-1292.5223}  
Iteration 3:{space 2}Log pseudolikelihood = {res:-1291.9435}  
Iteration 4:{space 2}Log pseudolikelihood = {res:-1291.9432}  
Iteration 5:{space 2}Log pseudolikelihood = {res:-1291.9432}  
{res}
{txt}{col 1}Ordered probit regression{col 57}{lalign 13:Number of obs}{col 70} = {res}{ralign 6:1,427}
{txt}{col 57}{lalign 13:Wald chi2({res:3})}{col 70} = {res}{ralign 6:586.01}
{txt}{col 57}{lalign 13:Prob > chi2}{col 70} = {res}{ralign 6:0.0000}
{txt}{col 1}{lalign 20:Log pseudolikelihood}{col 21} = {res}{ralign 10:-1291.9432}{txt}{col 57}{lalign 13:Pseudo R2}{col 70} = {res}{ralign 6:0.4060}

{txt}{ralign 97:(Std. err. adjusted for {res:357} clusters in {res:id})}
{hline 32}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 33}{c |}{col 45}    Robust
{col 1}                            pun{col 33}{c |} Coefficient{col 45}  std. err.{col 57}      z{col 65}   P>|z|{col 73}     [95% con{col 86}f. interval]
{hline 32}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 13}1.fightertreatment {c |}{col 33}{res}{space 2} 1.907496{col 45}{space 2} .0942622{col 56}{space 1}   20.24{col 65}{space 3}0.000{col 73}{space 4} 1.722745{col 86}{space 3} 2.092246
{txt}{space 15}1.childtreatment {c |}{col 33}{res}{space 2}-3.106533{col 45}{space 2} .1729796{col 56}{space 1}  -17.96{col 65}{space 3}0.000{col 73}{space 4}-3.445567{col 86}{space 3}-2.767499
{txt}{space 31} {c |}
fightertreatment#childtreatment {c |}
{space 27}1 1  {c |}{col 33}{res}{space 2} .7887852{col 45}{space 2}  .128763{col 56}{space 1}    6.13{col 65}{space 3}0.000{col 73}{space 4} .5364145{col 86}{space 3} 1.041156
{txt}{hline 32}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 26}/cut1 {c |}{col 33}{res}{space 2}-3.793033{col 45}{space 2} .2029796{col 73}{space 4}-4.190866{col 86}{space 3}  -3.3952
{txt}{space 26}/cut2 {c |}{col 33}{res}{space 2} -1.82696{col 45}{space 2} .1366534{col 73}{space 4}-2.094796{col 86}{space 3}-1.559125
{txt}{space 26}/cut3 {c |}{col 33}{res}{space 2}-.3176725{col 45}{space 2} .0587082{col 73}{space 4}-.4327384{col 86}{space 3}-.2026065
{txt}{space 26}/cut4 {c |}{col 33}{res}{space 2}  1.06428{col 45}{space 2} .0411999{col 73}{space 4} .9835301{col 86}{space 3} 1.145031
{txt}{hline 32}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. 
. *Model 2 (long format)
. oprobit pun fightertreatment##childtreatment ageofpunishment  female age education professional laborer unemployed income i.religion revimpreligion i.ethnicity revimpethnicity if pungroup<5, cluster(id)

{res}{txt}Iteration 0:{space 2}Log pseudolikelihood = {res:-2175.0596}  
Iteration 1:{space 2}Log pseudolikelihood = {res:-1320.3319}  
Iteration 2:{space 2}Log pseudolikelihood = {res:-1266.1332}  
Iteration 3:{space 2}Log pseudolikelihood = {res:-1265.4625}  
Iteration 4:{space 2}Log pseudolikelihood = {res:-1265.4619}  
Iteration 5:{space 2}Log pseudolikelihood = {res:-1265.4619}  
{res}
{txt}{col 1}Ordered probit regression{col 56}{lalign 13:Number of obs}{col 69} = {res}{ralign 7:1,427}
{txt}{col 56}{lalign 13:Wald chi2({res:19})}{col 69} = {res}{ralign 7:1046.05}
{txt}{col 56}{lalign 13:Prob > chi2}{col 69} = {res}{ralign 7:0.0000}
{txt}{col 1}{lalign 20:Log pseudolikelihood}{col 21} = {res}{ralign 10:-1265.4619}{txt}{col 56}{lalign 13:Pseudo R2}{col 69} = {res}{ralign 7:0.4182}

{txt}{ralign 97:(Std. err. adjusted for {res:357} clusters in {res:id})}
{hline 32}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 33}{c |}{col 45}    Robust
{col 1}                            pun{col 33}{c |} Coefficient{col 45}  std. err.{col 57}      z{col 65}   P>|z|{col 73}     [95% con{col 86}f. interval]
{hline 32}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 13}1.fightertreatment {c |}{col 33}{res}{space 2} 1.951028{col 45}{space 2} .0899444{col 56}{space 1}   21.69{col 65}{space 3}0.000{col 73}{space 4}  1.77474{col 86}{space 3} 2.127316
{txt}{space 15}1.childtreatment {c |}{col 33}{res}{space 2}-3.197693{col 45}{space 2} .1636781{col 56}{space 1}  -19.54{col 65}{space 3}0.000{col 73}{space 4}-3.518496{col 86}{space 3} -2.87689
{txt}{space 31} {c |}
fightertreatment#childtreatment {c |}
{space 27}1 1  {c |}{col 33}{res}{space 2} .8261132{col 45}{space 2} .1279654{col 56}{space 1}    6.46{col 65}{space 3}0.000{col 73}{space 4} .5753056{col 86}{space 3} 1.076921
{txt}{space 31} {c |}
{space 16}ageofpunishment {c |}{col 33}{res}{space 2} .1095142{col 45}{space 2} .0555515{col 56}{space 1}    1.97{col 65}{space 3}0.049{col 73}{space 4} .0006352{col 86}{space 3} .2183931
{txt}{space 25}female {c |}{col 33}{res}{space 2}-.4182222{col 45}{space 2} .1600375{col 56}{space 1}   -2.61{col 65}{space 3}0.009{col 73}{space 4}-.7318899{col 86}{space 3}-.1045544
{txt}{space 28}age {c |}{col 33}{res}{space 2}-.0038545{col 45}{space 2} .0039929{col 56}{space 1}   -0.97{col 65}{space 3}0.334{col 73}{space 4}-.0116804{col 86}{space 3} .0039714
{txt}{space 22}education {c |}{col 33}{res}{space 2} .0395892{col 45}{space 2} .0356313{col 56}{space 1}    1.11{col 65}{space 3}0.267{col 73}{space 4}-.0302468{col 86}{space 3} .1094253
{txt}{space 19}professional {c |}{col 33}{res}{space 2}-.1402192{col 45}{space 2}  .102702{col 56}{space 1}   -1.37{col 65}{space 3}0.172{col 73}{space 4}-.3415114{col 86}{space 3}  .061073
{txt}{space 24}laborer {c |}{col 33}{res}{space 2} .0300718{col 45}{space 2}  .094932{col 56}{space 1}    0.32{col 65}{space 3}0.751{col 73}{space 4}-.1559915{col 86}{space 3} .2161352
{txt}{space 21}unemployed {c |}{col 33}{res}{space 2} .1578173{col 45}{space 2} .1061869{col 56}{space 1}    1.49{col 65}{space 3}0.137{col 73}{space 4}-.0503052{col 86}{space 3} .3659399
{txt}{space 25}income {c |}{col 33}{res}{space 2} .0067767{col 45}{space 2}  .031304{col 56}{space 1}    0.22{col 65}{space 3}0.829{col 73}{space 4} -.054578{col 86}{space 3} .0681314
{txt}{space 31} {c |}
{space 23}religion {c |}
{space 26}Shia  {c |}{col 33}{res}{space 2}-.0796818{col 45}{space 2} .1442756{col 56}{space 1}   -0.55{col 65}{space 3}0.581{col 73}{space 4}-.3624567{col 86}{space 3} .2030932
{txt}{space 21}Christian  {c |}{col 33}{res}{space 2} .0062972{col 45}{space 2} .1972316{col 56}{space 1}    0.03{col 65}{space 3}0.975{col 73}{space 4}-.3802697{col 86}{space 3} .3928641
{txt}{space 25}Other  {c |}{col 33}{res}{space 2} .0430549{col 45}{space 2} .1488004{col 56}{space 1}    0.29{col 65}{space 3}0.772{col 73}{space 4}-.2485886{col 86}{space 3} .3346983
{txt}{space 31} {c |}
{space 17}revimpreligion {c |}{col 33}{res}{space 2} .1451794{col 45}{space 2} .0374076{col 56}{space 1}    3.88{col 65}{space 3}0.000{col 73}{space 4} .0718619{col 86}{space 3}  .218497
{txt}{space 31} {c |}
{space 22}ethnicity {c |}
{space 26}Kurd  {c |}{col 33}{res}{space 2} .1905065{col 45}{space 2}  .122205{col 56}{space 1}    1.56{col 65}{space 3}0.119{col 73}{space 4} -.049011{col 86}{space 3} .4300239
{txt}{space 23}Turkmen  {c |}{col 33}{res}{space 2} .1568676{col 45}{space 2}   .10825{col 56}{space 1}    1.45{col 65}{space 3}0.147{col 73}{space 4}-.0552986{col 86}{space 3} .3690338
{txt}{space 25}Other  {c |}{col 33}{res}{space 2}-.0081049{col 45}{space 2}  .191021{col 56}{space 1}   -0.04{col 65}{space 3}0.966{col 73}{space 4}-.3824992{col 86}{space 3} .3662895
{txt}{space 31} {c |}
{space 16}revimpethnicity {c |}{col 33}{res}{space 2}-.0390518{col 45}{space 2}  .040672{col 56}{space 1}   -0.96{col 65}{space 3}0.337{col 73}{space 4}-.1187675{col 86}{space 3} .0406639
{txt}{hline 32}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 26}/cut1 {c |}{col 33}{res}{space 2}-1.703447{col 45}{space 2} .9862671{col 73}{space 4}-3.636495{col 86}{space 3} .2296009
{txt}{space 26}/cut2 {c |}{col 33}{res}{space 2} .3262257{col 45}{space 2} .9754202{col 73}{space 4}-1.585563{col 86}{space 3} 2.238014
{txt}{space 26}/cut3 {c |}{col 33}{res}{space 2} 1.878361{col 45}{space 2} .9517688{col 73}{space 4} .0129281{col 86}{space 3} 3.743793
{txt}{space 26}/cut4 {c |}{col 33}{res}{space 2} 3.293735{col 45}{space 2} .9434122{col 73}{space 4} 1.444681{col 86}{space 3} 5.142789
{txt}{hline 32}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. 
. *Model 3 (see wide format)
. 
. *Manuscript Table 2. Robustness Check (Ordered Probit Regression) (see wide format)
. 
. *Robustness Checks for Locational Fixed Effects
. 
. *Punishment Preferences (OLS, Locational Effects)
. 
. reg pun fightertreatment##childtreatment westmosul ageofpunishment female age education professional laborer unemployed income i.religion revimpreligion i.ethnicity revimpethnicity if pungroup<5, cluster(id)

{txt}Linear regression                               Number of obs     = {res}     1,427
                                                {txt}F(20, 356)        =  {res}   334.63
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.7427
                                                {txt}Root MSE          =    {res} .62579

{txt}{ralign 97:(Std. err. adjusted for {res:357} clusters in {res:id})}
{hline 32}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 33}{c |}{col 45}    Robust
{col 1}                            pun{col 33}{c |} Coefficient{col 45}  std. err.{col 57}      t{col 65}   P>|t|{col 73}     [95% con{col 86}f. interval]
{hline 32}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 13}1.fightertreatment {c |}{col 33}{res}{space 2} 1.040068{col 45}{space 2} .0385607{col 56}{space 1}   26.97{col 65}{space 3}0.000{col 73}{space 4} .9642331{col 86}{space 3} 1.115904
{txt}{space 15}1.childtreatment {c |}{col 33}{res}{space 2}-1.884301{col 45}{space 2} .0380581{col 56}{space 1}  -49.51{col 65}{space 3}0.000{col 73}{space 4}-1.959148{col 86}{space 3}-1.809454
{txt}{space 31} {c |}
fightertreatment#childtreatment {c |}
{space 27}1 1  {c |}{col 33}{res}{space 2} .5537691{col 45}{space 2} .0466735{col 56}{space 1}   11.86{col 65}{space 3}0.000{col 73}{space 4} .4619787{col 86}{space 3} .6455596
{txt}{space 31} {c |}
{space 22}westmosul {c |}{col 33}{res}{space 2} -.112431{col 45}{space 2} .0880638{col 56}{space 1}   -1.28{col 65}{space 3}0.203{col 73}{space 4}-.2856218{col 86}{space 3} .0607597
{txt}{space 16}ageofpunishment {c |}{col 33}{res}{space 2} .0569563{col 45}{space 2} .0311814{col 56}{space 1}    1.83{col 65}{space 3}0.069{col 73}{space 4}-.0043665{col 86}{space 3} .1182792
{txt}{space 25}female {c |}{col 33}{res}{space 2}-.2142685{col 45}{space 2} .0881863{col 56}{space 1}   -2.43{col 65}{space 3}0.016{col 73}{space 4}-.3877001{col 86}{space 3}-.0408369
{txt}{space 28}age {c |}{col 33}{res}{space 2}-.0020149{col 45}{space 2} .0021186{col 56}{space 1}   -0.95{col 65}{space 3}0.342{col 73}{space 4}-.0061814{col 86}{space 3} .0021516
{txt}{space 22}education {c |}{col 33}{res}{space 2} .0174802{col 45}{space 2} .0189913{col 56}{space 1}    0.92{col 65}{space 3}0.358{col 73}{space 4}-.0198691{col 86}{space 3} .0548294
{txt}{space 19}professional {c |}{col 33}{res}{space 2}-.0720685{col 45}{space 2} .0536817{col 56}{space 1}   -1.34{col 65}{space 3}0.180{col 73}{space 4}-.1776417{col 86}{space 3} .0335046
{txt}{space 24}laborer {c |}{col 33}{res}{space 2} .0165872{col 45}{space 2} .0500322{col 56}{space 1}    0.33{col 65}{space 3}0.740{col 73}{space 4}-.0818086{col 86}{space 3} .1149829
{txt}{space 21}unemployed {c |}{col 33}{res}{space 2} .0779934{col 45}{space 2} .0553371{col 56}{space 1}    1.41{col 65}{space 3}0.160{col 73}{space 4}-.0308353{col 86}{space 3} .1868221
{txt}{space 25}income {c |}{col 33}{res}{space 2} .0026841{col 45}{space 2} .0163122{col 56}{space 1}    0.16{col 65}{space 3}0.869{col 73}{space 4}-.0293963{col 86}{space 3} .0347646
{txt}{space 31} {c |}
{space 23}religion {c |}
{space 26}Shia  {c |}{col 33}{res}{space 2}-.0453003{col 45}{space 2} .0761749{col 56}{space 1}   -0.59{col 65}{space 3}0.552{col 73}{space 4}-.1951096{col 86}{space 3} .1045089
{txt}{space 21}Christian  {c |}{col 33}{res}{space 2}  .030373{col 45}{space 2} .1049083{col 56}{space 1}    0.29{col 65}{space 3}0.772{col 73}{space 4} -.175945{col 86}{space 3}  .236691
{txt}{space 25}Other  {c |}{col 33}{res}{space 2} .0148036{col 45}{space 2} .0769224{col 56}{space 1}    0.19{col 65}{space 3}0.848{col 73}{space 4}-.1364757{col 86}{space 3}  .166083
{txt}{space 31} {c |}
{space 17}revimpreligion {c |}{col 33}{res}{space 2} .0789488{col 45}{space 2} .0203799{col 56}{space 1}    3.87{col 65}{space 3}0.000{col 73}{space 4} .0388687{col 86}{space 3} .1190289
{txt}{space 31} {c |}
{space 22}ethnicity {c |}
{space 26}Kurd  {c |}{col 33}{res}{space 2} .0985135{col 45}{space 2} .0636681{col 56}{space 1}    1.55{col 65}{space 3}0.123{col 73}{space 4}-.0266993{col 86}{space 3} .2237263
{txt}{space 23}Turkmen  {c |}{col 33}{res}{space 2} .0770257{col 45}{space 2} .0563882{col 56}{space 1}    1.37{col 65}{space 3}0.173{col 73}{space 4}-.0338701{col 86}{space 3} .1879215
{txt}{space 25}Other  {c |}{col 33}{res}{space 2} .0027642{col 45}{space 2} .0989852{col 56}{space 1}    0.03{col 65}{space 3}0.978{col 73}{space 4}-.1919051{col 86}{space 3} .1974335
{txt}{space 31} {c |}
{space 16}revimpethnicity {c |}{col 33}{res}{space 2}-.0207006{col 45}{space 2} .0214618{col 56}{space 1}   -0.96{col 65}{space 3}0.335{col 73}{space 4}-.0629085{col 86}{space 3} .0215072
{txt}{space 26}_cons {c |}{col 33}{res}{space 2} 2.599821{col 45}{space 2} .5380457{col 56}{space 1}    4.83{col 65}{space 3}0.000{col 73}{space 4} 1.541673{col 86}{space 3} 3.657969
{txt}{hline 32}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. 
. reg pun fightertreatment##childtreatment westmosul ageofpunishment female age education professional laborer unemployed income i.religion revimpreligion i.ethnicity revimpethnicity if pungroup<5, cluster(locale)

{txt}Linear regression                               Number of obs     = {res}     1,427
                                                {txt}F(20, 31)         =  {res}  4548.33
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.7427
                                                {txt}Root MSE          =    {res} .62579

{txt}{ralign 97:(Std. err. adjusted for {res:32} clusters in {res:locale})}
{hline 32}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 33}{c |}{col 45}    Robust
{col 1}                            pun{col 33}{c |} Coefficient{col 45}  std. err.{col 57}      t{col 65}   P>|t|{col 73}     [95% con{col 86}f. interval]
{hline 32}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 13}1.fightertreatment {c |}{col 33}{res}{space 2} 1.040068{col 45}{space 2} .0578738{col 56}{space 1}   17.97{col 65}{space 3}0.000{col 73}{space 4} .9220341{col 86}{space 3} 1.158103
{txt}{space 15}1.childtreatment {c |}{col 33}{res}{space 2}-1.884301{col 45}{space 2} .0506071{col 56}{space 1}  -37.23{col 65}{space 3}0.000{col 73}{space 4}-1.987515{col 86}{space 3}-1.781088
{txt}{space 31} {c |}
fightertreatment#childtreatment {c |}
{space 27}1 1  {c |}{col 33}{res}{space 2} .5537691{col 45}{space 2} .0720184{col 56}{space 1}    7.69{col 65}{space 3}0.000{col 73}{space 4} .4068867{col 86}{space 3} .7006516
{txt}{space 31} {c |}
{space 22}westmosul {c |}{col 33}{res}{space 2} -.112431{col 45}{space 2} .0927732{col 56}{space 1}   -1.21{col 65}{space 3}0.235{col 73}{space 4}-.3016432{col 86}{space 3} .0767811
{txt}{space 16}ageofpunishment {c |}{col 33}{res}{space 2} .0569563{col 45}{space 2} .0175037{col 56}{space 1}    3.25{col 65}{space 3}0.003{col 73}{space 4} .0212572{col 86}{space 3} .0926555
{txt}{space 25}female {c |}{col 33}{res}{space 2}-.2142685{col 45}{space 2} .0825779{col 56}{space 1}   -2.59{col 65}{space 3}0.014{col 73}{space 4}-.3826871{col 86}{space 3}-.0458498
{txt}{space 28}age {c |}{col 33}{res}{space 2}-.0020149{col 45}{space 2} .0034715{col 56}{space 1}   -0.58{col 65}{space 3}0.566{col 73}{space 4}-.0090952{col 86}{space 3} .0050653
{txt}{space 22}education {c |}{col 33}{res}{space 2} .0174802{col 45}{space 2} .0201673{col 56}{space 1}    0.87{col 65}{space 3}0.393{col 73}{space 4}-.0236512{col 86}{space 3} .0586116
{txt}{space 19}professional {c |}{col 33}{res}{space 2}-.0720685{col 45}{space 2} .0456691{col 56}{space 1}   -1.58{col 65}{space 3}0.125{col 73}{space 4}-.1652112{col 86}{space 3} .0210741
{txt}{space 24}laborer {c |}{col 33}{res}{space 2} .0165872{col 45}{space 2} .0277156{col 56}{space 1}    0.60{col 65}{space 3}0.554{col 73}{space 4}-.0399393{col 86}{space 3} .0731136
{txt}{space 21}unemployed {c |}{col 33}{res}{space 2} .0779934{col 45}{space 2} .0508101{col 56}{space 1}    1.53{col 65}{space 3}0.135{col 73}{space 4}-.0256345{col 86}{space 3} .1816213
{txt}{space 25}income {c |}{col 33}{res}{space 2} .0026841{col 45}{space 2} .0195289{col 56}{space 1}    0.14{col 65}{space 3}0.892{col 73}{space 4}-.0371453{col 86}{space 3} .0425136
{txt}{space 31} {c |}
{space 23}religion {c |}
{space 26}Shia  {c |}{col 33}{res}{space 2}-.0453003{col 45}{space 2}  .071216{col 56}{space 1}   -0.64{col 65}{space 3}0.529{col 73}{space 4}-.1905463{col 86}{space 3} .0999457
{txt}{space 21}Christian  {c |}{col 33}{res}{space 2}  .030373{col 45}{space 2} .1046519{col 56}{space 1}    0.29{col 65}{space 3}0.774{col 73}{space 4}-.1830659{col 86}{space 3}  .243812
{txt}{space 25}Other  {c |}{col 33}{res}{space 2} .0148036{col 45}{space 2} .0891576{col 56}{space 1}    0.17{col 65}{space 3}0.869{col 73}{space 4}-.1670344{col 86}{space 3} .1966416
{txt}{space 31} {c |}
{space 17}revimpreligion {c |}{col 33}{res}{space 2} .0789488{col 45}{space 2} .0220694{col 56}{space 1}    3.58{col 65}{space 3}0.001{col 73}{space 4}  .033938{col 86}{space 3} .1239596
{txt}{space 31} {c |}
{space 22}ethnicity {c |}
{space 26}Kurd  {c |}{col 33}{res}{space 2} .0985135{col 45}{space 2}  .059337{col 56}{space 1}    1.66{col 65}{space 3}0.107{col 73}{space 4}-.0225051{col 86}{space 3} .2195322
{txt}{space 23}Turkmen  {c |}{col 33}{res}{space 2} .0770257{col 45}{space 2} .0575647{col 56}{space 1}    1.34{col 65}{space 3}0.191{col 73}{space 4}-.0403782{col 86}{space 3} .1944296
{txt}{space 25}Other  {c |}{col 33}{res}{space 2} .0027642{col 45}{space 2} .1015353{col 56}{space 1}    0.03{col 65}{space 3}0.978{col 73}{space 4}-.2043184{col 86}{space 3} .2098467
{txt}{space 31} {c |}
{space 16}revimpethnicity {c |}{col 33}{res}{space 2}-.0207006{col 45}{space 2} .0274878{col 56}{space 1}   -0.75{col 65}{space 3}0.457{col 73}{space 4}-.0767623{col 86}{space 3}  .035361
{txt}{space 26}_cons {c |}{col 33}{res}{space 2} 2.599821{col 45}{space 2} .3529237{col 56}{space 1}    7.37{col 65}{space 3}0.000{col 73}{space 4} 1.880028{col 86}{space 3} 3.319614
{txt}{hline 32}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. 
. *Opposition to Reintegration into Society (OLS, Locational Effects) (see wide format)
. 
. log close
      {txt}name:  {res}<unnamed>
       {txt}log:  {res}C:\Users\swhitt\Desktop\TPV Children within Insurgency Replication Files\TPV Children within insurgency replication data long format log file.smcl
  {txt}log type:  {res}smcl
 {txt}closed on:  {res}11 Feb 2024, 17:33:51
{txt}{.-}
{smcl}
{txt}{sf}{ul off}